Personal trends module

ABSTRACT

A system and method for generating a personalized trends module includes steps of: for a given user, producing a social timeline by logging content posted on the given user&#39;s accounts on social media sites; analyzing the social timeline for recently posted content to derive an interim summary of first trending topics for the given user; receiving from a content personalization platform an in-stream feed of second trending topics based on the user&#39;s recent on-line activity including page views, queries, and clicks; augmenting the social timeline with the second trending topics from the in-stream feed to produce an interim list of third trending topics; ranking the third trending topics by source category using a frequency index; selecting the highest ranking third trending topics from each source category; and presenting a personalized trends module with positions allocated to the highest ranking third trending topics.

CROSS-REFERENCE TO RELATED APPLICATIONS

None.

FIELD OF THE INVENTION

The invention disclosed broadly relates to the field of on-line trendingtopics, and more particularly relates to the field of personalizedtrending topics.

BACKGROUND OF THE INVENTION

Trending topics are a popular on-line feature on many contentpublisher's websites, with captions such as: “Trending Now,” “NowTrending,” “Hot Topics,” “What's Trending” and others. Trending topicsprovide a list (usually just ten items) of the currently popular topicsin several categories such as news, entertainment, sports, finance, andjust about any other topic of interest to web users. Trending topicsgenerally appear in a prominent position on a content publisher's homepage as hyperlinks, making it easy for a user to identify what is“trending” now. The user can then click on one of the listed topic toview informative content for that topic. The trending topics are basedon web user's current interests, as derived from multiple data sources,such as search engines, social media sites, blogs, and other sources.

Referring now to the drawings and to FIG. 1 in particular, there isshown an example of a current “Trending Now” module 110 on a contentpublisher's home page (http://www.yahoo.com) 100. The “Trending Now”module features popular topics in news (finance, sports, celebrities,medicine), movies, books, consumer products, and many others. Thesetrends are curated for the publisher's region and/or content consumers.For example, an entertainment site would not feature re-financingtrends. The trending topics are a compilation of what interests thepublisher's consumers. In some cases, such as in the case of a searchengine such as Yahoo!®, the consumers are the general web population.Although the trending topics can be localized by region, and tailored toa publisher's consumers, they are not specific to an individual user.

Previous innovations are centered around ideas which consider trendingsearch terms as having higher than normal frequency of ‘search’/‘use’for a topic (United States Patent Publication Number 2012/0271829“System and Methods for Hot Topic Identification and Metadata,” filedApr. 25, 2011). A plurality of categories have also been identifiedbased on the intersection of categories viewed by the user and thecategories available via the system (U.S. Pat. No. 8,370,348, “MagazineEdition Recommendations,” filed Dec. 6, 2011). However, none of theexisting innovations provide trending topics customized for a user.

Therefore, there is a need for a method to enrich the user's on-lineexperience with customized trending topics.

SUMMARY OF THE INVENTION

Briefly, according to an embodiment of the present disclosure, a methodfor generating a personalized trends module includes steps or acts of:for a given user, producing a social timeline by logging content postedon the given user's accounts on social media sites; analyzing the socialtimeline for recently posted content to derive an interim summary offirst trending topics for the given user; receiving from a contentpersonalization platform an in-stream feed of second trending topicsbased on the user's recent on-line activity including page views,queries, and clicks; augmenting the social timeline with the secondtrending topics from the in-stream feed to produce an interim list ofthird trending topics; ranking the third trending topics by sourcecategory using a frequency index; selecting the highest ranking thirdtrending topics from each source category; and presenting a personalizedtrends module with positions allocated to the highest ranking thirdtrending topics.

According to another embodiment of the present disclosure, aninformation processing system includes a processor device operablycoupled with a memory. The memory includes computer-executableinstructions that, when executed, cause a computer to perform steps of:for a given user, producing a social timeline by logging content postedon the given user's accounts on social media sites; analyzing the socialtimeline for recently posted content to derive an interim summary offirst trending topics for the given user; receiving from a contentpersonalization platform an in-stream feed of second trending topicsbased on the user's recent on-line activity including page views,queries, and clicks; augmenting the social timeline with the secondtrending topics from the in-stream feed to produce an interim list ofthird trending topics; ranking the third trending topics by sourcecategory using a frequency index; selecting the highest ranking thirdtrending topics from each source category; and presenting a personalizedtrends module with positions allocated to the highest ranking thirdtrending topics.

According to another embodiment of the present disclosure, a computerprogram product with a non-transitory computer-readable storage mediumincludes computer-executable instructions for performing the methodsteps above.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To describe the foregoing and other exemplary purposes, aspects, andadvantages, we use the following detailed description of an exemplaryembodiment of the disclosure with reference to the drawings, in which:

FIG. 1 is an exemplary screenshot depicting the state of the art intrending topics, according to the known art;

FIG. 2 is an exemplary screenshot depicting personalized trends,according to an embodiment of the present disclosure;

FIG. 3 is a close-up view of the personalized trends of FIG. 2,according to an embodiment of the present disclosure;

FIG. 4 is a simplified schematic diagram of the system for personalizingtrends, according to an embodiment of the present disclosure;

FIG. 5 is a high-level flowchart of the method for generatingpersonalized trends, according to an embodiment of the presentdisclosure;

FIG. 6A is an exemplary entry log according to an embodiment of thepresent disclosure;

FIG. 6B is an exemplary table of <user,term,rank> triples, according toan embodiment of the present disclosure;

FIG. 7 is a simplified block diagram of the mobile applicationembodiment, according to the present disclosure;

FIG. 8 is a lower-level flowchart of the analyzing step 540 from theflowchart of FIG. 5, according to an embodiment of the presentdisclosure;

FIG. 9 is a lower-level flowchart of the listing step 550 from theflowchart of FIG. 5, according to an embodiment of the presentdisclosure;

FIG. 10 shows an exemplary illustration of how a user's social mediaaccount is linked to the personal trending system, according to anembodiment of the present disclosure; and

FIG. 11 shows a screenshot of BuzzFeed, a trending topic website,according to the known art; and

FIG. 12 is a simplified block diagram of the hardware componentsrequired to implement the personal trending methodology, according to anembodiment of the present disclosure.

While the disclosure as claimed can be modified into alternative forms,specific embodiments thereof are shown by way of example in the drawingsand will herein be described in detail. It should be understood,however, that the drawings and detailed description thereto are notintended to limit the disclosure to the particular form disclosed, buton the contrary, the intention is to cover all modifications,equivalents and alternatives falling within the scope of the presentdisclosure.

DETAILED DESCRIPTION

Before describing in detail embodiments that are in accordance with thepresent disclosure, it should be observed that the embodiments resideprimarily in combinations of method steps and system components relatedto systems and methods for placing computation inside a communicationnetwork. Accordingly, the system components and method steps have beenrepresented where appropriate by conventional symbols in the drawings,showing only those specific details that are pertinent to understandingthe embodiments of the present disclosure so as not to obscure thedisclosure with details that will be readily apparent to those ofordinary skill in the art having the benefit of the description herein.Thus, it will be appreciated that for simplicity and clarity ofillustration, common and well-understood elements that are useful ornecessary in a commercially feasible embodiment may not be depicted inorder to facilitate a less obstructed view of these various embodiments.

We describe an automated and personalized system and method forgenerating up-to-date trending topics that are customized for a givenuser. The personal trending topics are customized, meaning that we listthe topics that have been curated specifically for the given user,rather than just listing global and/or locale-specific trending topics,as is currently done. We identify trending topics that are currently ofinterest to a given user, based on the user's own communications to/fromhis/her social contacts; and the user's web activity. In an embodimentof the present disclosure, the personal trending module highlightstrending terms and topics based on the user's social media streamscombined with search engine queries, blogs, posts, and the like.

For a given user, we require as an initial input into the process theuser's identification, and the user's location (<user,location>). Afterlinking to the user's social media accounts such as Facebook® (“FB”) andTwitter®, we access the social media accounts to retrieve content postedto/from the user on the social media sites. The social content is thencombined with an in stream feed of locally trending topics that arestreamed from an intelligent personalization platform such asSlingstone. The personalization platform uses known methods to derivetopics that are of interest to a given location, by aggregating webactivity which can take the form of page views, “shares,” “saves,” andothers. From this combination an interim list is produced, providing alist of topics that are relevant to a given user at a given location.The interim list is then augmented by at least one top trending topicthat is “hot” across the Web, received from the publisher's own HomePage, or from a trending site such as BuzzFeed®. See FIG. 11 for ascreenshot of BuzzFeed's Home Page.

The personal trends process collects, combines, and summarizes signalsfrom multiple streams in three categories: social (from FB and Twitter),in-stream (from SlingStone), and local/global (from BuzzFeed). Thesocial input is derived from social media sites such as Twitter®, FB,and LinkedIn. The in-stream input is derived from contentpersonalization engines such as Slingstone. The local/global input isderived from trending topic sites such as BuzzFeed that are constantlyupdating trending topics. In current trending topic modules, the topicsreported as “trending now” are based on the topic categories discussedby the people whom the user follows. The personal trends methodologyaccording to the present disclosure, however, employs a unique algorithmthat builds a summary of multiple streams at any point in time. The keydifference between the personal trends methodology and the state of theart can be illustrated with a simple example, as follows.

Using the current trending technology, assume a user follows “BillGates” on Twitter®. The current trending methodology assigns a trendtopic of “Technology” to the user, meaning that every news-worthy techtrend will show up as a trending topic on the user's “Trending Now”section, even though the user may have been following Bill Gates becauseof an interest in his philanthropic pursuits.

Now we discuss how the personal trends module differs from the currentmethod by more accurately personalizing the user's trending topics. Weassume the same initial scenario (a user follows “Bill Gates” onTwitter®). Bill Gates tweets about “Relief program for flood victims.”According to the personal trends methodology, “Relief program for floodvictims” is captured in the user's Twitter® stream for the currenttimeline and parsed to extract the term “flood victims.” This term“flood victims” is now a candidate for a personalized trending topicbecause this Tweet is part of the user's current timeline (as streamedfrom the user's Twitter® account).

The difference apparent to the user is in what appears on the TrendingTopics list (“Technology” vs. “Flood Victims”). The difference in themethodology is in the capturing of a timeline summary for a point intime, rather than just a keyword match to a topic, or a local feed oftrending topics. With the current methodology for trending topics,“flood victims” would only make it into the top 10 trends if a largeamount of the users within the user's location (or throughout the Web)discuss this same issue. To recap, the personal trends are trends basedon the combined view of a timeline from the user's different on-linestreams, which can change at every minute. To this end, we canincorporate trending topic detection applications as part of ouralgorithm.

The present disclosure will now be described with respect to FIGS. 2through 12 which are block diagrams and flowchart illustrations ofembodiments of the present disclosure. It will be understood that eachblock of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions. Theflowchart and block diagrams in the Figures illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present disclosure.

In this regard, each block in the flowchart or block diagrams mayrepresent a module, segment, or portion of code, which comprises one ormore executable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

Personal Trends Module.

Referring now to FIG. 3 there is shown a close-up view of thepersonalized “Trending Now” module 250, according to an embodiment ofthe present disclosure. In this example, the module's placement followsthe conventional placement for trending topics, but one with knowledgein the art will understand that the placement can vary within the spiritand scope of the disclosure. For example, the module 250 can bedisplayed as a banner across the top. In this example the PersonalizedModule 250 includes five different sections:

1. A sponsored trend ad 252 such as the Iron Man 3 trailer in position 3(source=advertiser);

2. Local trends 254—Stanford Holi, Tamil new year Sunnyvale, May daycelebrations in positions 1, 6, and 8 (source=content personalizationpipeline);

3. Web Activity-based trend stream 255—Pain and Gain reviews, Netflixpricing, EA layoffs in positions 2, 5, and 10 (source=contentpersonalization pipeline);

4. Behavioral trends 256—Duck dynasty record, Jennifer Love Hewitt inpositions 9 and 7 (source=social media)

5. Global trends 258—Scandal Revelations in position 4 (source=publisherhome page).

The module 250 as shown in FIG. 3 also includes video data 330 relatedto one of the trending topics. Optionally, the module 250 can includethe user's name or sign-in 340. This information becomes available whenthe user's accounts are linked with the publisher.

Back-End Processing.

The underlying engine retrieves the content shared by the user on socialmedia sites such as FB and Twitter® to generate social trending topicscustomized for the user. Ideally, we link the user's social mediaaccounts to the publisher site where the trends are displayed. FIG. 10shows one example of how this can be done. Once we have access to theuser's social media accounts, we log the content that appears on theuser's site. This can be both content originated by the user or contentfrom others that is posted on the user's site.

We log, analyze, classify, and rank these entries for a period of timeto produce a social timeline of social trending topics. The timeline isthen combined with local trending streams from a content personalizationpipeline such as Slingstone and also combined with global trending termsfrom the publisher to create an enriched, customized experience withpersonal trends displayed in the publisher's Home Page.

Personal Trends Schematic.

Referring now to FIG. 4 there is shown a high-level schematic diagram400 of the personal trends system, according to an embodiment of thepresent disclosure. The initial input to the process is a tuple 405 ofthe user and the user's location <user,location> as shown in FIG. 6. TheSocial Profiling Module 420, running an application program interface425, uses this identification of the user to access the user's on-linenetworks, such as the social media accounts FB and Twitter®. Usingstreaming feeds 415 from the user's social media accounts. The SocialProfiling Module 420 logs, parses and analyzes the current social mediacontent and activity associated with the user for a given period of timeto extract terms of interest. For example, the user's Twitter® “tweets”are parsed to extract terms of interest. The user's FB “Likes” are alsoparsed, as are any comments posted on the user's “Wall” in FB. Otherstreams which can be analyzed are LinkedIn®, Yahoo! Home Run stream,Reddit, Digg, and others.

The Social Profiling Module 420 parses the streams to extract terms ofinterest and logs each occurrence of a term of interest, the timestamp,and from what source it originated. After parsing the input streams toextract trending terms and logging the trending terms with the number ofoccurrences, the Social Profiling Module 420 outputs a timeline with theuser's current topics of interest from social media. Added to thistimeline are the in-stream topics derived from a content personalizationplatform. These in-stream topics provide the currently trending topicsas derived from the user's on-line activity (page views, clicks,queries). This provides a comprehensive view of topics that are relevantto the user at the current time.

Next, the Mapping/Ranking Module 450 receives the timeline (which isreally a log of all of the terms of interest, where they originated, andtheir number of occurrences). The Mapping/Ranking Module 450 assignsranks to the terms using a frequency index. After ranking the terms, theMapping/Ranking Module 450 outputs a pre-defined number n of terms foreach category (social and in-stream trends) by triples of<user,term,rank> in each category. The ranking is based on a “buzz”score which is an index of the frequency with which the terms appear.

At this point in the process we have ranked social and in-stream trends.Now we introduce the local/global trends which are prepared by a numberof sites such as BuzzFeed. These feeds are updated frequently andgenerally localized. Within a large market like the United States, thetrend feeds can be localized to a region or even a city. In smallermarkets, the feeds are localized to the country as whole.

Now that the Mapping/Ranking Module 450 has two source categories ofcontent, the module 450 also ranks the categories by their occurrencesto output a triple for <user,category,rank>. The first set of triples<user,term,rank> provides a list of ranked terms from which we are ableto select the highest ranked terms. The second set of triples<user,category,rank> provides a ranked list of categories, ranked byusage. Let's say we define n as 150; therefore we produce up to (n/2)ranked terms for each of the two initial categories (social, in-stream).The categories themselves are ranked by usage. For example, if most ofthe input for the current timeline is retrieved from social media, thenthe social category is ranked highest for this timeline. From thisinterim list of n triples, we select the n_(p) top-ranked personalterms.

In one embodiment of the present disclosure, we weight the categories byoccurrence and thereby select a subset n_(p) of triples <user,term,rank>by weighted category. For example, if most of the timeline entries occurin the social category, then out of n_(p) possible terms, we select ahigher proportion of them to be social terms. However, if the user's FBactivity has been very light, we can rely more heavily on the in-streamfeeds.

The Mapping/Ranking Module 450 outputs the n_(p) triples 455 to theCombining Module 460. The Combining Module 460 combines thehighest-ranked topics from the two categories (social, in-stream) on aperiodic basis. The Combining Module 460 also receives as input the topn_(G) trending topics associated with the user's location<location,term,rank> from a publisher site or a trending site such asBuzzNews. The personal trending algorithm can interface with knowntrending topic detection applications to receive the top n_(G) trendingtopics by location.

Assuming a final personal trends module containing n_(T)=10 topics, weselect from one to five of the highest ranked trending local/globaltopics G to combine with between five to nine of the highest rankedtriples 455 P from the social and in-stream categories, for a total of10 T Trending Topics (or whatever number has been selected for display).The output is a list 468 of the top n_(T) personal trends T customizedfor the user, including local/global trending topics G that are trendingwithin the web community. In an embodiment of the present invention, thelist includes 10 trends, but this amount can vary. The Personal TrendingModule 470 receives this list 468 and interfaces with the publisher toserve the user's personal trends 250 on the Home Page 480. As seen withthe example of FIG. 3, we are also able to reserve a position for asponsored trend.

Methodology.

Referring now to FIG. 5, there is shown a high-level flowchart 500 ofthe method for generating a personalized trending module, according toan embodiment of the present invention. In step 510 we link a user'ssocial media sites, such as Facebook® and Twitter®, to a publisher sitesuch as Yahoo! FIG. 10 shows an example of how a user can sign up tolink sites. We use connected accounts wherein the user has registeredand logs in to access the accounts. We do this so that we can acquireaccess to the content shared on the user's social media sites. Byaccessing the social media sites, we can stream in the content shared bythe user and the user's contacts. This provides an indication of theuser's current interests.

We acknowledge that any recent web activity by the user (apart from thesocial media) can also provide insight into the user's interests,therefore we interface with a known content personalization platform toreceive in-stream feeds of trending topics based on the user's Internetactivity (queries, clicks, views). Once we have access to the socialmedia content and the in-stream feeds, we parse the input to extractterms of interest in step 520. In step 530 we build the timeline 430 bylogging the entries by user id (from the login sessions) and include thesource category (social or in-stream Web activity trends).

We derive social trending signals from the user's perspective byanalyzing the timeline 430 in the Mapping/Ranking Module 450 in step540. This includes ranking the terms and the categories to outputtriples of <user,term,rank> and <user,category,rank>. We use contentanalysis tools such as yql.content.analyze from Yahoo!® From thetimeline 430 we retrieve the highest-ranked current n_(p) topics/termsthat are interesting to the user (trending now for the user) as gleanedfrom the shared content on the user's social media sites. For example,assume the user is from the Tamil ethnic group native to India and SriLanka. From the user's posts on social media sites, we log multiplereferences to “Puthandu,” which is the Tamil New Year, as shown in FIG.6A. With a ranking index of 1 to 10, with 10 being the highest, weassign the highest rank to this term because of its many occurrences inthe social media streams (<user1,Puthandu,10>). See FIG. 6B. Given thatthe Tamils make up such a small percentage of the world population,“Tamil New Year” is not likely to be a “Trending Now” topic undercurrent methods. However, because the references to “Puthandu” werecaptured from the user's current social streams, we are able to provide“Tamil New Year” as a trending topic for this user.

Referring now to FIG. 8, we show a lower-level flowchart of step 540.First in step 542 we parse the timeline, performing semantic analysis toextract terms that may be associated with topics of interest. We canemploy known trending topic detection algorithms to extract the terms.Once we have the topics of interest, we rank the topics by theirfrequency of occurrence in the timeline 430. Lastly we output the nranked topics of interest in a list of triples of the format<user,term,rank>.

Returning now to FIG. 5, we group the n topics into two categories(social and Web activity trends) to produce an interim list of the toptrends that are significant for a given user. In step 550 we select thetop n_(p) trending topics for the user from the interim list. In step560 we combine the interim list of the n_(p) user's personal toptrending topics with n_(G) global/local trending topics from the frontpage of the publisher's site, or from another venue such as TimeSense orBuzzFeed. We now rank the list of topics from all three sourcecategories (local/global, social, Web activity) The combined list ofn_(T) (n_(G)+n_(p)) provides trends that are customized for the user.

In step 570 we present the list of n_(T) personalized trends to theuser; an example of which is shown in FIG. 2. In order to distinguishthe user-derived trends from the global trends, we can present one setin a distinguishable manner, perhaps by using different text colors,font, highlighting, placement, or the like. Referring again to FIG. 3 wecan distinguish the different categories of topics using color (notshown here), font, size, shading, highlighting, and the like.

In step 580 we periodically update the topics to keep them fresh andrelevant. We can update every hour, every half-hour, quarter-hour or asoften as deemed necessary. The updating step involves running steps 520through 570 at periodic intervals. We can stagger the updates so that weperform updates on the timeline for the Web activity and social topicsat a different rate than we update the global/local trending streams.The updates can be tied into the level of activity on the social mediaand other sites. For example, if there is a burst of activity, weperform the updates more often. Every update period, at batch intervals,we retrieve the timeline 430 for the user. We analyze the timeline 430and select the terms/current trend categories that appear interesting tothe user.

As updates occur we can adjust the number n_(G) of global trends thatappear on the Home Page 480 to increase or decrease depending on theuser involvement with the personalized top 10 topics 250. Because we logthe user's clicks from the Home Page 480, we are able to receivefeedback on the user's perceived relevance of the topics selected by thepersonal trends algorithm. For example, if we list “Iron Man 3 trailer”on the personalized top 10 topics and the user subsequently clicks onthat item to view more information or to buy movie tickets, then “IronMan 3 trailer” will appear on the subsequent timeline from the Webactivity stream, thus validating its selection.

Likewise, if “Iron Man 3 trailer” is listed on the personalized top 10topics and no activity in either social media or Web activity for “IronMan 3” appears in subsequent timelines, it is not likely to stay in thetop 10.

Monetization.

In an embodiment of the present disclosure, we can include one or morepositions for sponsored trends within the list of top personal trends250. The one or more positions can be sold for placement of search orlistings ads either using location-based or interest-based categories.The sponsored ad position(s) can become a new ad-unit, called sponsoredtrends. The personal trends module 250 as shown in FIG. 3 can alsoinclude advertisements, such as the Iron Man 3 trailer 330.

In another embodiment of the present disclosure, the updates to generatea current list of the top personal trends 250 can be done as a servicefor a third party such as an advertiser or publisher. In this embodimentthe module 250 will most likely include a sponsored ad and/or anadvertisement.

Mobile App Embodiment.

Referring now to FIG. 7, using the Mobile App Gateway 750, we are ableserve personal trends on a user's mobile device 710. The user device 710simply accesses the Mobile App Gateway 750 through known protocols inorder to use the Personal Trends System 400.

Benefits and Advantages of the Disclosure.

With trending topics that are personalized for a user, the time spent ona publisher's home page trending now stories will increase as users seemore relevance in the trending topics. Personalized topics, because theyappear more relevant to the user, will lead to higher CTRs(click-through rates) and therefore larger revenue and search traffic.Sponsored trends will become a new ad-format that will drive morerevenue to advertisers and publishers.

We enrich the quality of trending news for the user by filtering outother ‘hot topics’ which might not be relevant to the user, therebygenerating more clicks/revenue per click. We engage the user byproviding him/her with the ability to view content of his/her choiceover a period of time. As the users' choices change it is reflected inthe personal trending module.

Hardware Embodiment.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, computer-implemented method orcomputer program product. Accordingly, aspects of the present disclosuremay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and hardware aspects that mayall generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or morenon-transitory computer readable medium(s) having computer readableprogram code embodied thereon.

A computer readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

It will be understood that each block of the foregoing flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor device of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor device of the computer or other programmable data processingapparatus, cause a computer to implement the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerprogram instructions may also be stored in a computer readable mediumthat can direct a computer, other programmable data processingapparatus, or other devices to function in a particular manner, suchthat the instructions stored in the computer readable medium produce anarticle of manufacture including instructions which implement thefunction/act specified in the flowchart and/or block diagram block orblocks. The computer program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other devicesto cause a series of operational steps to be performed on the computer,other programmable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Referring now to FIG. 12, there is provided a simplified pictorialillustration of a computerized information processing system 1200 forgenerating Personal Trends in which the present disclosure may beimplemented. For purposes of this disclosure, computer system 1200 mayrepresent any type of computer, information processing system or otherprogrammable electronic device, including a client computer, a servercomputer, a portable computer, an embedded controller, a personaldigital assistant, and so on. The computer system 1200 may be astand-alone device, but is more than likely networked into a largersystem. Computer system 1200, illustrated for exemplary purposes as anetworked computing device, is in communication with other networkedcomputing devices (not shown) via network interface 1214. As will beappreciated by those of ordinary skill in the art, network interface1214 may be embodied using conventional networking technologies and mayinclude one or more of the following: local area networks, wide areanetworks, intranets, public Internet 1290 and the like.

In general, the routines which are executed when implementing theseembodiments, whether implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions, will be referred to herein as computer programs, or simplyprograms. The computer programs typically include one or moreinstructions that are resident at various times in various memory andstorage devices in an information processing or handling system such asa computer, and that, when read and executed by one or more processors,cause that system to perform the steps necessary to execute steps orelements embodying the various aspects of the invention.

Throughout the description herein, an embodiment of the invention isillustrated with aspects of the invention embodied solely on computersystem 1200. As will be appreciated by those of ordinary skill in theart, aspects of the invention may be distributed amongst one or morenetworked computing devices which interact with computer system 100 viaone or more data networks such as, for example, network 1214. However,for ease of understanding, aspects of the invention have been embodiedin a single computing device—computer system 1200.

Computer system 1200 includes inter alia processing device 1202 whichcommunicates with an input/output subsystem 1206, memory 1204, storage1212 and network 1214. The processor device 1202 is operably coupledwith a communication infrastructure 1229 (e.g., a communications bus,cross-over bar, or network). The processor device 1202 may be a generalor special purpose microprocessor operating under control of computerprogram instructions executed from memory 1204 on program data. Theprocessor device 1202 may include a number of special purposesub-processors such as a comparator engine, each sub-processor forexecuting particular portions of the computer program instructions. Eachsub-processor may be a separate circuit able to operate substantially inparallel with the other sub-processors.

The memory 1204 may be partitioned or otherwise mapped to reflect theboundaries of the various memory subcomponents. Memory 1204 may includeboth volatile and persistent memory for the storage of: operationalinstructions for execution by CPU 1202, data registers, applicationstorage and the like. Memory 104 preferably includes a combination ofrandom access memory (RAM), read only memory (ROM) and persistent memorysuch as that provided by a hard disk drive. The computerinstructions/applications that are stored in memory 1204 are executed byprocessor 1202. The computer instructions/applications and program datacan also be stored in a hard disk drive or mass storage 1212 forexecution by processor device 1202.

Those skilled in the art will appreciate that the functionalityimplemented within the blocks illustrated in the diagram may beimplemented as separate components or the functionality of several orall of the blocks may be implemented within a single component. Forexample, the functionality for the Combining Module 460 may be includedin the same component as the Mapping/Ranking Module 450.

The I/O subsystem 1206 may comprise various end user interfaces such asa display, a keyboards, and a mouse. The I/O subsystem 1206 may furtherinclude an I/O interface 1210, a connection to a network 1214 such as alocal-area network (LAN) or wide-area network (WAN) such as the Internet1290.

The computer system 1200 may also include a removable storage drive1216, representing a floppy disk drive, a magnetic tape drive, anoptical disk drive, etc. The removable storage drive 1216 reads fromand/or writes to a removable storage unit 1201 in a manner well known tothose having ordinary skill in the art. Removable storage unit 1201,represents a floppy disk, a compact disc, magnetic tape, optical disk,CD-ROM, DVD-ROM, etc. which is read by and written to by removablestorage drive 1216. As will be appreciated, the removable storage unit1201 includes a non-transitory computer readable medium having storedtherein computer software and/or data.

The computer system 1200 may also include a communications interface1212. Communications interface 1212 allows software and data to betransferred between the computer system and external devices. Examplesof communications interface 1212 may include a modem, a networkinterface (such as an Ethernet card), a communications port, a PCMCIAslot and card, etc. Software and data transferred via communicationsinterface 1212 are in the form of signals which may be, for example,electronic, electromagnetic, optical, or other signals capable of beingreceived by communications interface 1212.

In this document, the terms “computer program medium,” “computer usablemedium,” and “computer readable medium” are used to generally refer toboth transitory and non-transitory media such as main memory 1204,removable storage drive 1216, a hard disk installed in hard disk drive,and signals. These computer program products are means for providingsoftware to the computer system 1200. The computer readable medium 1201allows the computer system 1200 to read data, instructions, messages ormessage packets, and other computer readable information from thecomputer readable medium 1201.

The system 1200 interfaces with search engines 1245 and contentpersonalization platforms 1244 through the Internet 1290. It should alsobe noted that storage 1212, though shown here as residing solely withinthe computer 1200, may be a combination of local and remote storage. Thestorage 1212 is used to store timelines, logs and may include look-uptables, graphs, charts, and the like.

Therefore, while there has been described what is presently consideredto be the preferred embodiment, it will understood by those skilled inthe art that other modifications can be made within the spirit of theinvention. The above description(s) of embodiment(s) is not intended tobe exhaustive or limiting in scope. The embodiment(s), as described,were chosen in order to explain the principles of the invention, showits practical application, and enable those with ordinary skill in theart to understand how to make and use the invention. It should beunderstood that the invention is not limited to the embodiment(s)described above, but rather should be interpreted within the fullmeaning and scope of the appended claims.

We claim:
 1. A computer-implemented method for generating personalizedtrending topics, comprising: using a processor device, performing stepsof: for a given user, producing a social timeline by logging contentposted on the given user's accounts on social media sites; analyzing thesocial timeline for recently posted content to derive an interim summaryof first trending topics for the given user; receiving an in-stream feedof second trending topics based on the user's recent on-line activityincluding page views, queries, and clicks; augmenting the socialtimeline with the second trending topics from the in-stream feed toproduce an interim list of third trending topics; ranking the thirdtrending topics by source category using a frequency index; selecting asubset of highest ranked third trending topics from each sourcecategory; and presenting a personalized trends module comprisingpositions allocated to the subset of the highest ranked third trendingtopics.
 2. The computer-implemented method of claim 1 furthercomprising: ranking the source categories by the frequency index;wherein the source categories comprise social media and on-lineactivity.
 3. The computer-implemented method of claim 2 whereinselecting the subset of the highest ranked third trending topicscomprises: weighting the selection proportionately to the ranking of thesource categories, such that the source category with more occurrencesin the timeline is allocated more positions in the personalized topicsmodule than a source category with fewer occurrences in the timeline. 4.The computer-implemented method of claim 3 further comprising:allocating a position for at least one global trending topic from apublisher's site to the personalized trends module; retrieving the atleast one global trending topic; and adding the at least one globaltrending topic before presenting the personalized trends module.
 5. Thecomputer-implemented method of claim 4 wherein presenting thepersonalized trends module comprises presenting the topics such that thefirst trending topics, the second trending topics, the third trendingtopics, and the at least one global trending topic are distinguishableby assigning a different appearance to the topics based on sourcecategory.
 6. The computer-implemented method of claim 4 furthercomprising: allocating a position for a sponsored trending topic in thepersonalized trends module; and adding the sponsored trending topic tothe personalized trends module.
 7. The computer-implemented method ofclaim 4 further comprising: adding an advertisement to the personalizedtrends module.
 8. The computer-implemented method of claim 4 furthercomprising: adding an identifier of the given user to the personalizedtrends module.
 9. The computer-implemented method of claim 4 furthercomprising: performing the analyzing, receiving, augmenting, ranking,selecting, and presenting steps at periodic intervals.
 10. Thecomputer-implemented method of claim 4 further comprising: performingthe analyzing, receiving, augmenting, ranking, selecting, and presentingsteps at random intervals, based on the user's on-line activity.
 11. Thecomputer-implemented method of claim 9 wherein performing the steps atperiodic intervals further comprises performing the steps of retrievingand adding the at least one global trending topic at different periodicintervals.
 12. An information processing system for generatingpersonalized trending topics comprises: a processor device; and a memoryoperably coupled with the processor device, said memory comprisingcomputer-executable instructions causing a computer to perform steps of:for a given user, producing a social timeline by logging content postedon the given user's accounts on social media sites; analyzing the socialtimeline for recently posted content to derive an interim summary offirst trending topics for the user; receiving an in-stream feed ofsecond trending topics based on the user's recent on-line activity suchas page views, queries, and clicks; augmenting the social timeline withthe second trending topics from the in-stream feed to produce an interimlist of third trending topics; ranking the third trending topics bysource category using a frequency index; selecting a subset of highestranked third trending topics from each source category; and presenting apersonalized trends module comprising positions allocated to the subsetof the highest ranked third trending topics.
 13. The informationprocessing system of claim 12 wherein the computer-executableinstructions further comprise instructions for: ranking the sourcecategories by the frequency index; wherein the source categoriescomprise social media and on-line activity; and wherein selecting thesubset of the highest ranked third trending topics comprises weightingthe selection proportionately to the ranking of the source categories,such that the source category with more occurrences in the timeline isallocated more positions in the personalized topics module than a sourcecategory with fewer occurrences in the timeline.
 14. The informationprocessing system of claim 13 wherein the computer-executableinstructions further comprise instructions for: allocating a positionfor at least one global trending topic from a publisher's site to thepersonalized trends module; retrieving the at least one global trendingtopic; and adding the at least one global trending topic beforepresenting the personalized trends module.
 15. The informationprocessing system of claim 13 wherein the computer-executableinstructions further comprise instructions for: presenting the topics inthe personalized trends module such that the first trending topics, thesecond trending topics, the third trending topics, and the at least oneglobal trending topic are distinguishable by assigning a differentappearance to the topics based on source category.
 16. The informationprocessing system of claim 13 wherein the computer-executableinstructions further comprise instructions for: allocating a positionfor a sponsored trending topic in the personalized trends module; andadding the sponsored trending topic to the personalized trends module.17. The information processing system of claim 13 wherein thecomputer-executable instructions further comprise instructions for:adding an advertisement to the personalized trends module.
 18. Theinformation processing system of claim 13 wherein thecomputer-executable instructions further comprise instructions for:adding an identifier of the given user to the personalized trendsmodule.
 19. A computer program product comprising a non-transitorycomputer-readable storage medium with computer-executable instructionsstored thereon, said computer-executable instructions, when executed,causing a computer to perform steps of: for a given user, producing asocial timeline by logging content posted on the given user's accountson social media sites; analyzing the social timeline for recently postedcontent to derive an interim summary of first trending topics for theuser; receiving an in-stream feed of second trending topics based on theuser's recent on-line activity such as page views, queries, and clicks;augmenting the social timeline with the second trending topics from thein-stream feed to produce an interim list of third trending topics;ranking the third trending topics by source category using a frequencyindex; selecting a subset of highest ranked third trending topics fromeach source category; and presenting a personalized trends modulecomprising positions allocated to the subset of the highest ranked thirdtrending topics.
 20. The computer program product of claim 19 whereinthe computer-executable instructions further cause a computer toperform: ranking the source categories by the frequency index; whereinthe source categories comprise social media and on-line activity; andwherein selecting the subset of the highest ranked third trending topicscomprises weighting the selection proportionately to the ranking of thesource categories, such that the source category with more occurrencesin the timeline is allocated more positions in the personalized topicsmodule than a source category with fewer occurrences in the timeline.